| name | cognitive-rhythm-writing |
|---|---|
| description | 説明的な文章に緩急を設計するための規範。緩急を装飾ではなく認知モードの切替（観察→逡巡→断定→再観察）と未回収の緊張の管理として扱い、文の拍、段落の密度波形、節の入り方、緩みと駄文の判別、執筆後の機械的な点検手順を定める。読み物として読ませたい章・記事・解説文を生成するとき、または「密度はあるが平坦でおもしろくない」文章を診断・修正するときに使用する。 |
密度の高い文章が退屈になるのは、情報が多いからではなく、全文が同じ認知モードで書かれているからである。 この規範は、読者の認知モード（観察する、迷う、確信する、確かめ直す）を意図的に切り替え、常に「続きを読む理由」を維持することで、読み進める推進力を作る。
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| const titleInput = document.getElementById("title"); | |
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| const tasks = await res.json(); | |
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| for (const task of tasks) { | |
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A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
This is a very brief tutorial on getting OpenWRT installed on a regular computer, it assumes you know your way around Linux. If you find this and need additional details, please like, subscribe, and comm... oh wait this isn't youtube, just comment.
Updated: Current version is 22.03.5, this was originally written for 18.6.x in 2019.
The session model (Fable) is the orchestrator, not the worker. Fable is the most expensive tier; spend its tokens only on decomposition, judgment calls, synthesis, and talking to the user. Delegate everything else to subagents via the Agent tool, picking the cheapest model that can do the job well:
model: "opus" — the default worker tier. Anything requiring real judgment: implementation, debugging, architecture-aware exploration, adversarial review.
model: "sonnet" — cheap tier for mechanical or low-stakes work: running tests and reporting output, simple greps/lookups with a known target, rote refactors from an exact spec, formatting, screenshot capture, admin chores. If getting it slightly wrong is cheap to catch, use sonnet.
Exploration/research: never read broadly yourself. Spawn Explore agents (model: opus) with tightly scoped questions; consume their synthesized reports, not raw files. Trivial "find the file that defines X" lookups can go to sonnet.
Stop re-explaining yourself every session. This prompt makes Claude interview you (one question at a time, checkboxes included) and then compile everything into a personal CLAUDE.md — so every new session starts like Claude already knows you.
Interview me to build my personal CLAUDE.md context file.
Rules: